Overview

Dataset statistics

Number of variables11
Number of observations599
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory51.6 KiB
Average record size in memory88.2 B

Variable types

Text1
Numeric8
Categorical2

Alerts

ID has unique valuesUnique
PRG has 93 (15.5%) zerosZeros
PR has 28 (4.7%) zerosZeros
SK has 175 (29.2%) zerosZeros
TS has 289 (48.2%) zerosZeros
M11 has 9 (1.5%) zerosZeros

Reproduction

Analysis started2024-03-25 12:59:27.668597
Analysis finished2024-03-25 13:00:02.886095
Duration35.22 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

ID
Text

UNIQUE 

Distinct599
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2024-03-25T13:00:03.311764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters5391
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique599 ?
Unique (%)100.0%

Sample

1st rowICU200010
2nd rowICU200011
3rd rowICU200012
4th rowICU200013
5th rowICU200014
ValueCountFrequency (%)
icu200010 1
 
0.2%
icu200083 1
 
0.2%
icu200012 1
 
0.2%
icu200013 1
 
0.2%
icu200014 1
 
0.2%
icu200015 1
 
0.2%
icu200016 1
 
0.2%
icu200017 1
 
0.2%
icu200018 1
 
0.2%
icu200019 1
 
0.2%
Other values (589) 589
98.3%
2024-03-25T13:00:04.362077image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1407
26.1%
2 819
15.2%
I 599
11.1%
C 599
11.1%
U 599
11.1%
1 220
 
4.1%
4 220
 
4.1%
3 220
 
4.1%
5 220
 
4.1%
6 129
 
2.4%
Other values (3) 359
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3594
66.7%
Uppercase Letter 1797
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1407
39.1%
2 819
22.8%
1 220
 
6.1%
4 220
 
6.1%
3 220
 
6.1%
5 220
 
6.1%
6 129
 
3.6%
7 120
 
3.3%
8 120
 
3.3%
9 119
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
I 599
33.3%
C 599
33.3%
U 599
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 3594
66.7%
Latin 1797
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1407
39.1%
2 819
22.8%
1 220
 
6.1%
4 220
 
6.1%
3 220
 
6.1%
5 220
 
6.1%
6 129
 
3.6%
7 120
 
3.3%
8 120
 
3.3%
9 119
 
3.3%
Latin
ValueCountFrequency (%)
I 599
33.3%
C 599
33.3%
U 599
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5391
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1407
26.1%
2 819
15.2%
I 599
11.1%
C 599
11.1%
U 599
11.1%
1 220
 
4.1%
4 220
 
4.1%
3 220
 
4.1%
5 220
 
4.1%
6 129
 
2.4%
Other values (3) 359
 
6.7%

PRG
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8247078
Minimum0
Maximum17
Zeros93
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2024-03-25T13:00:04.853652image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.362839
Coefficient of variation (CV)0.87924075
Kurtosis0.28990902
Mean3.8247078
Median Absolute Deviation (MAD)2
Skewness0.91400756
Sum2291
Variance11.308686
MonotonicityNot monotonic
2024-03-25T13:00:05.164217image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 100
16.7%
0 93
15.5%
2 76
12.7%
3 59
9.8%
4 54
9.0%
5 49
8.2%
7 37
 
6.2%
6 37
 
6.2%
8 32
 
5.3%
9 20
 
3.3%
Other values (7) 42
7.0%
ValueCountFrequency (%)
0 93
15.5%
1 100
16.7%
2 76
12.7%
3 59
9.8%
4 54
9.0%
5 49
8.2%
6 37
 
6.2%
7 37
 
6.2%
8 32
 
5.3%
9 20
 
3.3%
ValueCountFrequency (%)
17 1
 
0.2%
15 1
 
0.2%
14 2
 
0.3%
13 7
 
1.2%
12 8
 
1.3%
11 7
 
1.2%
10 16
2.7%
9 20
3.3%
8 32
5.3%
7 37
6.2%

PL
Real number (ℝ)

Distinct129
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.15359
Minimum0
Maximum198
Zeros5
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2024-03-25T13:00:05.560528image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77.9
Q199
median116
Q3140
95-th percentile181
Maximum198
Range198
Interquartile range (IQR)41

Descriptive statistics

Standard deviation32.682364
Coefficient of variation (CV)0.27200489
Kurtosis0.75640184
Mean120.15359
Median Absolute Deviation (MAD)21
Skewness0.11617993
Sum71972
Variance1068.1369
MonotonicityNot monotonic
2024-03-25T13:00:05.970099image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 15
 
2.5%
99 14
 
2.3%
125 11
 
1.8%
95 11
 
1.8%
105 11
 
1.8%
122 10
 
1.7%
119 10
 
1.7%
109 10
 
1.7%
111 10
 
1.7%
84 10
 
1.7%
Other values (119) 487
81.3%
ValueCountFrequency (%)
0 5
0.8%
44 1
 
0.2%
57 2
 
0.3%
61 1
 
0.2%
62 1
 
0.2%
67 1
 
0.2%
68 1
 
0.2%
71 4
0.7%
72 1
 
0.2%
73 3
0.5%
ValueCountFrequency (%)
198 1
 
0.2%
197 4
0.7%
196 3
0.5%
195 1
 
0.2%
194 3
0.5%
193 2
0.3%
191 1
 
0.2%
189 4
0.7%
188 2
0.3%
187 2
0.3%

PR
Real number (ℝ)

ZEROS 

Distinct44
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.732888
Minimum0
Maximum122
Zeros28
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2024-03-25T13:00:06.368214image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q164
median70
Q380
95-th percentile90
Maximum122
Range122
Interquartile range (IQR)16

Descriptive statistics

Standard deviation19.335675
Coefficient of variation (CV)0.2813162
Kurtosis5.2588834
Mean68.732888
Median Absolute Deviation (MAD)8
Skewness-1.8746617
Sum41171
Variance373.86833
MonotonicityNot monotonic
2024-03-25T13:00:06.822726image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
70 46
 
7.7%
68 41
 
6.8%
74 38
 
6.3%
72 37
 
6.2%
64 37
 
6.2%
78 31
 
5.2%
80 30
 
5.0%
66 29
 
4.8%
76 29
 
4.8%
0 28
 
4.7%
Other values (34) 253
42.2%
ValueCountFrequency (%)
0 28
4.7%
24 1
 
0.2%
30 2
 
0.3%
40 1
 
0.2%
44 3
 
0.5%
46 1
 
0.2%
48 5
 
0.8%
50 10
 
1.7%
52 8
 
1.3%
54 8
 
1.3%
ValueCountFrequency (%)
122 1
 
0.2%
110 3
0.5%
108 2
0.3%
104 2
0.3%
102 1
 
0.2%
100 2
0.3%
98 3
0.5%
96 3
0.5%
95 1
 
0.2%
94 2
0.3%

SK
Real number (ℝ)

ZEROS 

Distinct51
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.562604
Minimum0
Maximum99
Zeros175
Zeros (%)29.2%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2024-03-25T13:00:07.732657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q332
95-th percentile44
Maximum99
Range99
Interquartile range (IQR)32

Descriptive statistics

Standard deviation16.017622
Coefficient of variation (CV)0.77896855
Kurtosis-0.31425268
Mean20.562604
Median Absolute Deviation (MAD)12
Skewness0.16406327
Sum12317
Variance256.56422
MonotonicityNot monotonic
2024-03-25T13:00:08.732636image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 175
29.2%
32 25
 
4.2%
30 24
 
4.0%
33 17
 
2.8%
23 17
 
2.8%
28 17
 
2.8%
31 15
 
2.5%
18 15
 
2.5%
15 14
 
2.3%
27 14
 
2.3%
Other values (41) 266
44.4%
ValueCountFrequency (%)
0 175
29.2%
7 2
 
0.3%
8 2
 
0.3%
10 4
 
0.7%
11 5
 
0.8%
12 6
 
1.0%
13 8
 
1.3%
14 6
 
1.0%
15 14
 
2.3%
16 6
 
1.0%
ValueCountFrequency (%)
99 1
 
0.2%
63 1
 
0.2%
60 1
 
0.2%
56 1
 
0.2%
54 2
0.3%
52 2
0.3%
51 1
 
0.2%
50 3
0.5%
49 2
0.3%
48 2
0.3%

TS
Real number (ℝ)

ZEROS 

Distinct164
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.460768
Minimum0
Maximum846
Zeros289
Zeros (%)48.2%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2024-03-25T13:00:09.733370image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median36
Q3123.5
95-th percentile293.7
Maximum846
Range846
Interquartile range (IQR)123.5

Descriptive statistics

Standard deviation116.57618
Coefficient of variation (CV)1.467091
Kurtosis8.0889556
Mean79.460768
Median Absolute Deviation (MAD)36
Skewness2.4015846
Sum47597
Variance13590.005
MonotonicityNot monotonic
2024-03-25T13:00:10.272023image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 289
48.2%
140 8
 
1.3%
130 7
 
1.2%
105 7
 
1.2%
94 6
 
1.0%
115 5
 
0.8%
56 5
 
0.8%
120 5
 
0.8%
210 5
 
0.8%
76 5
 
0.8%
Other values (154) 257
42.9%
ValueCountFrequency (%)
0 289
48.2%
14 1
 
0.2%
18 2
 
0.3%
23 2
 
0.3%
25 1
 
0.2%
29 1
 
0.2%
32 1
 
0.2%
36 3
 
0.5%
37 2
 
0.3%
38 1
 
0.2%
ValueCountFrequency (%)
846 1
0.2%
744 1
0.2%
680 1
0.2%
600 1
0.2%
579 1
0.2%
545 1
0.2%
543 1
0.2%
495 2
0.3%
485 1
0.2%
480 1
0.2%

M11
Real number (ℝ)

ZEROS 

Distinct233
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.920033
Minimum0
Maximum67.1
Zeros9
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2024-03-25T13:00:11.000850image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.1
Q127.1
median32
Q336.55
95-th percentile45.02
Maximum67.1
Range67.1
Interquartile range (IQR)9.45

Descriptive statistics

Standard deviation8.0082273
Coefficient of variation (CV)0.25088405
Kurtosis3.2610266
Mean31.920033
Median Absolute Deviation (MAD)4.7
Skewness-0.40525495
Sum19120.1
Variance64.131705
MonotonicityNot monotonic
2024-03-25T13:00:11.533246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 11
 
1.8%
31.6 11
 
1.8%
33.3 10
 
1.7%
31.2 9
 
1.5%
0 9
 
1.5%
29.7 8
 
1.3%
33.6 7
 
1.2%
30.8 7
 
1.2%
32.9 7
 
1.2%
30.1 7
 
1.2%
Other values (223) 513
85.6%
ValueCountFrequency (%)
0 9
1.5%
18.2 3
 
0.5%
18.4 1
 
0.2%
19.1 1
 
0.2%
19.3 1
 
0.2%
19.4 1
 
0.2%
19.6 3
 
0.5%
19.9 1
 
0.2%
20 1
 
0.2%
20.4 2
 
0.3%
ValueCountFrequency (%)
67.1 1
0.2%
59.4 1
0.2%
55 1
0.2%
53.2 1
0.2%
52.9 1
0.2%
52.3 2
0.3%
50 1
0.2%
49.7 1
0.2%
48.8 1
0.2%
48.3 1
0.2%

BD2
Real number (ℝ)

Distinct437
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48118698
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2024-03-25T13:00:12.013599image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.1418
Q10.248
median0.383
Q30.647
95-th percentile1.1279
Maximum2.42
Range2.342
Interquartile range (IQR)0.399

Descriptive statistics

Standard deviation0.33755235
Coefficient of variation (CV)0.70149934
Kurtosis6.1146739
Mean0.48118698
Median Absolute Deviation (MAD)0.171
Skewness1.9894723
Sum288.231
Variance0.11394159
MonotonicityNot monotonic
2024-03-25T13:00:12.414318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.254 6
 
1.0%
0.258 5
 
0.8%
0.268 4
 
0.7%
0.687 4
 
0.7%
0.299 4
 
0.7%
0.237 4
 
0.7%
0.259 4
 
0.7%
0.238 4
 
0.7%
0.207 4
 
0.7%
0.151 3
 
0.5%
Other values (427) 557
93.0%
ValueCountFrequency (%)
0.078 1
0.2%
0.084 1
0.2%
0.085 2
0.3%
0.088 2
0.3%
0.089 1
0.2%
0.092 1
0.2%
0.096 1
0.2%
0.101 1
0.2%
0.102 1
0.2%
0.107 1
0.2%
ValueCountFrequency (%)
2.42 1
0.2%
2.329 1
0.2%
2.288 1
0.2%
2.137 1
0.2%
1.893 1
0.2%
1.781 1
0.2%
1.731 1
0.2%
1.699 1
0.2%
1.6 1
0.2%
1.476 1
0.2%

Age
Real number (ℝ)

Distinct50
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.290484
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 KiB
2024-03-25T13:00:13.103529image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q340
95-th percentile58.1
Maximum81
Range60
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.828446
Coefficient of variation (CV)0.35531012
Kurtosis0.69227058
Mean33.290484
Median Absolute Deviation (MAD)7
Skewness1.1523529
Sum19941
Variance139.91213
MonotonicityNot monotonic
2024-03-25T13:00:13.479062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 52
 
8.7%
21 52
 
8.7%
25 40
 
6.7%
24 37
 
6.2%
23 27
 
4.5%
29 27
 
4.5%
28 26
 
4.3%
26 25
 
4.2%
27 21
 
3.5%
41 20
 
3.3%
Other values (40) 272
45.4%
ValueCountFrequency (%)
21 52
8.7%
22 52
8.7%
23 27
4.5%
24 37
6.2%
25 40
6.7%
26 25
4.2%
27 21
3.5%
28 26
4.3%
29 27
4.5%
30 16
 
2.7%
ValueCountFrequency (%)
81 1
 
0.2%
72 1
 
0.2%
69 1
 
0.2%
67 3
0.5%
66 3
0.5%
65 3
0.5%
64 1
 
0.2%
63 3
0.5%
62 4
0.7%
61 2
0.3%

Insurance
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
1
411 
0
188 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters599
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 411
68.6%
0 188
31.4%

Length

2024-03-25T13:00:13.928728image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T13:00:14.360630image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 411
68.6%
0 188
31.4%

Most occurring characters

ValueCountFrequency (%)
1 411
68.6%
0 188
31.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 599
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 411
68.6%
0 188
31.4%

Most occurring scripts

ValueCountFrequency (%)
Common 599
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 411
68.6%
0 188
31.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 599
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 411
68.6%
0 188
31.4%

Sepssis
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
Negative
391 
Positive
208 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters4792
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPositive
2nd rowNegative
3rd rowPositive
4th rowNegative
5th rowPositive

Common Values

ValueCountFrequency (%)
Negative 391
65.3%
Positive 208
34.7%

Length

2024-03-25T13:00:14.650160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-25T13:00:14.887333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
negative 391
65.3%
positive 208
34.7%

Most occurring characters

ValueCountFrequency (%)
e 990
20.7%
i 807
16.8%
t 599
12.5%
v 599
12.5%
N 391
 
8.2%
g 391
 
8.2%
a 391
 
8.2%
P 208
 
4.3%
o 208
 
4.3%
s 208
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4193
87.5%
Uppercase Letter 599
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 990
23.6%
i 807
19.2%
t 599
14.3%
v 599
14.3%
g 391
 
9.3%
a 391
 
9.3%
o 208
 
5.0%
s 208
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
N 391
65.3%
P 208
34.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 4792
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 990
20.7%
i 807
16.8%
t 599
12.5%
v 599
12.5%
N 391
 
8.2%
g 391
 
8.2%
a 391
 
8.2%
P 208
 
4.3%
o 208
 
4.3%
s 208
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4792
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 990
20.7%
i 807
16.8%
t 599
12.5%
v 599
12.5%
N 391
 
8.2%
g 391
 
8.2%
a 391
 
8.2%
P 208
 
4.3%
o 208
 
4.3%
s 208
 
4.3%

Interactions

2024-03-25T12:59:59.801344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:36.548731image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:41.134363image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:45.077178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:49.925631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:52.385888image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:54.596188image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:56.908367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T13:00:00.124181image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:37.008229image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:41.633121image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:45.546262image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:50.354655image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:52.700493image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:54.877286image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:57.162976image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T13:00:00.448651image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:37.428767image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:42.147470image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:46.585136image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:50.733780image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:53.075769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:55.277619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:57.594249image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T13:00:00.711336image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:37.941806image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:42.725982image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:47.031628image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:51.010047image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:53.342224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:55.568150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:58.059067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T13:00:00.969731image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:38.713467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:43.144322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:48.098722image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:51.269920image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:53.586538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:55.822543image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:58.724120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T13:00:01.202754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:39.463834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:43.582906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:48.681579image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:51.513449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:53.803290image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:56.080968image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:58.958833image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T13:00:01.470444image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:40.184255image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:44.031797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:49.082614image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:51.868572image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:54.071933image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:56.376204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:59.201001image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T13:00:01.733875image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:40.687947image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:44.482071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:49.643534image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:52.112245image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:54.328735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:56.635044image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-25T12:59:59.436262image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-25T13:00:02.195134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-25T13:00:02.683700image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDPRGPLPRSKTSM11BD2AgeInsuranceSepssis
0ICU20001061487235033.60.627500Positive
1ICU2000111856629026.60.351310Negative
2ICU2000128183640023.30.672321Positive
3ICU20001318966239428.10.167211Negative
4ICU2000140137403516843.12.288331Positive
5ICU2000155116740025.60.201301Negative
6ICU20001637850328831.00.248260Positive
7ICU2000171011500035.30.134291Negative
8ICU2000182197704554330.50.158531Positive
9ICU200019812596000.00.232541Positive
IDPRGPLPRSKTSM11BD2AgeInsuranceSepssis
589ICU20059907300021.10.342250Negative
590ICU200600111118440046.80.925450Positive
591ICU2006012112785014039.40.175240Negative
592ICU2006023132800034.40.402440Positive
593ICU200603282522211528.51.699250Negative
594ICU2006046123724523033.60.733340Negative
595ICU2006050188821418532.00.682221Positive
596ICU200606067760045.30.194461Negative
597ICU20060718924192527.80.559210Negative
598ICU2006081173740036.80.088381Positive